Search Results

With the widespread use of GPS devices and the advancement of location-based services, a vast amount of trajectory data has been collected and mined for various applications. Trajectory clustering, which categorizes trajectories into distinct groups, is ...

The automatic generation of radiological imaging reports aims to produce accurate and coherent clinical descriptions based on X-ray images. This facilitates clinicians in completing the arduous task of report writing and advances clinical automation. The ...

Parameter server is widely used in distributed machine learning to accelerate training. However, the increasing heterogeneity of workers’ computing capabilities leads to the issue of stragglers, making parameter synchronization challenging. To ...

The rapid progress of foundation models has led to the prosperity of autonomous agents, which leverage the universal capabilities of foundation models to conduct reasoning, decision-making, and environmental interaction. However, the efficacy of agents ...

Given the critical role of graphs in real-world applications and their high-security requirements, improving the ability of graph neural networks (GNNs) to detect out-of-distribution (OOD) data is an urgent research problem. The recent work GNNSAFE (Wu ...

Data sharing faces significant constraints with business competition intensifying and privacy regulations enacted. Secure collaborative analysis, particularly in group-by-aggregation queries, has gained significant attention as a way to perform ...

Skyline is a primitive operation in multi-criteria decision applications and there is an increasing demand to support such operations over a data federation, where the entire dataset is separately held by multiple data providers (a.k.a., silos), ...

The Maximizing Range Sum (MaxRS) query plays a pivotal role in computational geometry and location-aware applications. This query aims to optimally place a rectangle within a set of weighted objects to maximize the total weight of the objects it ...$$$$$$

Author name disambiguation (AND) is a central task in academic search, which has received more attention recently accompanied by the increase of authors and academic publications. To tackle the AND problem, existing studies have proposed various ...

In the process of crowdsourced testing, massive bug reports are submitted. Among them, the severity level of the bug report is an important indicator for traigers of crowdsourced platforms to arrange the order of reports efficiently so that ...

SPHGCN‐S, a crowdsourced bug, reports severity prediction model that integrates text and screenshot information via a heterogeneous GCN. SPHGCN‐S enables the interaction between global vocabulary and document information by designing three weight ...

Future wireless networks are envisioned to provide ubiquitous sensing services, driving a substantial demand for multi-dimensional non-convex parameter estimation. This entails dealing with non-convex likelihood functions containing numerous local optima. ...

Knowledge-grounded dialogue (KGD) learns to generate an informative response based on a given dialogue context and external knowledge (e.g., knowledge graphs; KGs). Recently, the emergence of large language models (LLMs) and pre-training techniques has ...

Recently, channel extrapolation has been widely investigated in frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. However, in time-division duplex (TDD) fifth generation (5G) new radio (NR) systems, the channel ...

We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where near-field propagation and spatial non-stationary effects exist. In this case, each ...

Deep actor-critic (DAC) algorithms, which combine actor-critic with deep neural network (DNN), have been among the most prevalent reinforcement learning algorithms for decision-making problems in simulated environments. However, the existing DAC ...

Efficient learning and model compression algorithm for deep neural network (DNN) is a key workhorse behind the rise of deep learning (DL). In this work, we propose a message passing-based Bayesian deep learning algorithm called EM-TDAMP to avoid the ...

In this paper, we present a novel hybrid precoding scheme, named group-and-codebook based two-timescale hybrid precoding (GC-THP), for reducing complexity in millimeter wave massive MIMO systems. The scheme clusters users into groups based on their ...

Recently, massive multiple input multiple output (MIMO) channel tracking in the fifth generation (5G) new radio (NR) systems has attracted intensive interest. By exploiting the dynamic sparsity of massive MIMO channels, it is possible to design a high-...

We focus on visible light communication-based simultaneous position and orientation detection (SPAO) for user devices (UDs) using photodiodes, which is challenging due to scattering interference and small-scale fading. To address this challenge, a novel ...

Poster layout is a crucial aspect of poster design. Prior methods primarily focus on the correlation between visual content and graphic elements. However, a pleasant layout should also consider the relationship between visual and textual contents and the ...